Modellierung der Stromaufnahme von Weichenantrieben

Die Stromaufnahme eines Weichenmotors weist über die einzelnen Weichenumläufe hinweg ein stochastisches Verhalten auf und hängt von vielen verschiedenen Faktoren ab. Ziel des vorliegenden Beitrags ist die Modellierung der zeitlichen Veränderungen der Stromstärke beim Stellstrom mit Methoden der Zeitreihenanalyse unter Einbeziehung exogener Variablen wie z.B. der Lufttemperatur. Dies ermöglicht in der Folge die Erkennung und ggf. Prognose anormalen Anlagenverhaltens

Transparente Fehlerdiagnose bei Weichenstörungen mittels Bayes'scher Netze

Die Anwendung moderner Verfahren der künstlichen Intelligenz (KI) zu Diagnosezwecken erfordert insbesondere bei sicherheitskritischen Anlagen eine sorgfältige Betrachtung der Verlässlichkeit der Ergebnisse. Bayes'sche Netze als etablierte KI-Methode weisen in dieser Hinsicht aufgrund ihrer Transparenz und eines hohen Maßes an Nachvollziehbarkeit der mit ihrer Hilfe algorithmisch realisierten, diagnostischen Schlussfolgerungen einige sehr positive Merkmale auf. Der vorliegende Beitrag diskutiert und demonstriert das theoretische und praktische Potenzial Bayes'scher Netze für die Fehlerdiagnose bei Weichenstörungen anhand konkreter Beispiele

Entwicklung und Validierung von Anomaliedetektionsverfahren für Weichen

Das Überwachungssystem POSS® von Strukton Rail (SR) erfasst kontinuierlich den Stromverbrauch von Weichenantrieben, um deren Zustand zu überwachen. Basierend auf dieser Datengrundlage werden Wartungsmaßnahmen geplant. Dieser Beitrag berichtet über die gemeinsamen Forschungsaktivitäten von DLR und SR zur Entwicklung und Verbesserung von Algorithmen zur automatisierten Fehlererkennung. Ziel ist es, Anomalien und Fehler der Weichen zu erkennen, Warnmeldungen zu generieren und diese in ein umfassendes Zustandsüberwachungssystem zu integrieren. Hierzu werden in enger Kooperation Verfahren entwickelt, die das Expertenwissen der Wartungsanalytiker berücksichtigen. Die Verfahren, ihre prototypische Implementierung im SR Kontrollzentrum und erste Ergebnisse der Validierung werden nachfolgend vorgestellt

The importance of bubble deformability for strong drag reduction in bubbly turbulent Taylor-Couette flow

Bubbly turbulent Taylor-Couette (TC) flow is globally and locally studied at Reynolds numbers of Re = 5 x 10^5 to 2 x 10^6 with a stationary outer cylinder and a mean bubble diameter around 1 mm. We measure the drag reduction (DR) based on the global dimensional torque as a function of the global gas volume fraction a_global over the range 0% to 4%. We observe a moderate DR of up to 7% for Re = 5.1 x 10^5. Significantly stronger DR is achieved for Re = 1.0 x 10^6 and 2.0 x 10^6 with, remarkably, more than 40% of DR at Re = 2.0 x 10^6 and a_global = 4%. To shed light on the two apparently different regimes of moderate DR and strong DR, we investigate the local liquid flow velocity and the local bubble statistics, in particular the radial gas concentration profiles and the bubble size distribution, for the two different cases; Re = 5.1 x 10^5 in the moderate DR regime and Re = 1.0 x 10^6 in the strong DR regime, both at a_global = 3 +/- 0.5%. By defining and measuring a local bubble Weber number (We) in the TC gap close to the IC wall, we observe that the crossover from the moderate to the strong DR regime occurs roughly at the crossover of We ~ 1. In the strong DR regime at Re = 1.0 x 10^6 we find We > 1, reaching a value of 9 (+7, -2) when approaching the inner wall, indicating that the bubbles increasingly deform as they draw near the inner wall. In the moderate DR regime at Re = 5.1 x 10^5 we find We ~ 1, indicating more rigid bubbles, even though the mean bubble diameter is larger, namely 1.2 (+0.7, -0.1) mm, as compared to the Re = 1.0 x 10^6 case, where it is 0.9 (+0.6, -0.1) mm. We conclude that bubble deformability is a relevant mechanism behind the observed strong DR. These local results match and extend the conclusions from the global flow experiments as found by van den Berg et al. (2005) and from the numerical simulations by Lu, Fernandez & Tryggvason (2005).Comment: 31 pages, 17 figure

Vapor nucleation, dynamics and heat flux in Rayleigh-Bénard convection

Experimental work on 2-phase turbulent Rayleigh-Bénard convection was conducted. Vapor bubbles uniquely nucleated at cylindrical micro-cavities etched on the superheated bottom plate (bp). Five different cavity separations (cs) were studied. Turbulent convection (1-phase flow) under the same thermal forcing as for the 2-phase flow was the reference. The circular heated/etched area located in the center of the bp was smaller than the cooling area. \ud \ud Vapor bubbles led to heat-flux enhancement (hfe), which increased with larger superheat and depended weakly on the cavity number. At a given large superheat, the hfe per active site increased with decreasing active site density and it saturated for the case of a very small density. \ud \ud The bubbles affected the bulk temperature; in 2-phase we found a more stable thermal gradient than in 1-phase flow, a reduction of the temperature drop across the thermal boundary layer (bl) of the bp, and a reduction of the temperature standard deviation. The hfe correlated with these effects in the bulk. The skewness of the temperature pdf was positive and constant in 1-phase flow, and was increasingly reduced in 2-phase flow for increasing bp superheat.\ud \ud Blocking the large-scale circulation (LSC) from the nucleating area on one hand, and isolating the liquid column above it from the rest of the flow on the other, led to an even larger hfe. \ud \ud Lateral shadowgraph visualization of 1-phase flow showed plumes not forming a well-defined LSC. In 2-phase flow the LSC was better defined, and the reduced skewness for large superheat associated to the flow dynamics.\ud \ud Bubbles condensed rapidly after departure due to large gradients across the bl. Close to the bp the LSC dragged the bubbles horizontally, decelerating them. In the bulk, their condensation rate and vertical deceleration were constant. There was a large difference between bubble and LSC velocities. The travelled distance until bubbles fully condensed correlated to the maximal vertical velocity along single bubble trajectories.\ud \ud At the bp bubble volume grew linearly with time and at departure neither strongly depended on superheat nor on cs. For all cs the departure frequency exponentially increased as a function of superheat. For a given superheat, the larger the cs was, the more irregular in time they departed. The latent heat required for bubble growth contributed up 25% to the enhanced heat transferred by the surface. The contribution due to effective buoyancy increased with superheat

Bayesian network design for fault diagnostics of railway switches

Besides detecting failures and predicting future health conditions of technical systems, fault diagnosis (i.e., fault identification) is a key challenge in the analytic part of prognostics and health management (PHM). In this context, Bayesian networks (BN) has proven to be an effective tool for diagnostic reasoning about faults and effects. Since it is possible to generate such models not only from data but also from expert knowledge or a combination of both (hybrid approach), Bayesian networks are well-suited for many applications and (technical) disciplines. This, in particular, holds for situations where common data-driven approaches (e.g., neural networks, deep learning) suffer from a lack of a reasonable amount of adequate training data. This contribution discusses the detailed design of a comprehensive Bayesian network for railway switches as to be used for fault diagnosis in context of corrective and/or predictive maintenance, for instance. The new model explicitly pursues the modular paradigm of object-oriented Bayesian networks (OOBN), and thus provides a maximum degree of flexibility when adapting it to different types of railway switches. Moreover, it contains Bayesian nodes that act as a kind of "ON/OFF switches" and allow to (de-)activate specific parts of the model without affecting its overall structure. This, in particular, is useful whenever the general Bayesian network comprises modules (e.g., point heater or back drive) that are not available to all switches in the field. Finally, the model benefits from a newly developed, innovative design principle for Bayesian networks which, based on a generalization of the idea of Boolean clusters, reduces (or potentially even completely avoids) the problematic effect of overconfidence in diagnostic reasoning

Statistical process control model for switch failure detection and maintenance effectiveness assessment

Railway switches are crucial not only for normal operation of the railroad system as they guide trains to a track or platform, but also when disruptions occur since they allow trains to take alternative routes. Switch components and functions require frequent inspection, maintenance and renewal, making of switches a costly asset. The switch moving parts are subject to high deterioration and prone to malfunctioning, posing a safety hazard if no immediate action is taken. Nowadays online condition monitoring, standardization of inspection and maintenance actions, as well as data-based models are some of the tools supporting decision making for preventive planning, cost reduction and process effectiveness. This contribution presents a data-based model (derived from features extracted from measured point engine current during switch blade movement) for switch status nowcast and forecast applying statistical process control (SPC) methods. The SPC model is capable of identifying abnormal switch behavior; through examples it will be demonstrated how emerging failures in an early stage of development can be detected without the need of a labelled training data set of historical failures. The SPC model offers advantages over commonly used monitoring systems, as it does not rely on manually set switch-specific thresholds and references to detect the switch blades movements used to trigger alarms in these systems. Switch maintenance takes place regularly, sometimes significantly affecting the switch functional normal behaviour. Thus maintenance restricts somewhat the applicability of the SPC model. This contribution includes the discussion of methods applied for integrating the maintenance actions into the model. In turn, the SPC model output is used to assess the effectiveness of maintenance and the completeness of the reported actions performed on the switch. This work is partly funded by the EU H2020 and Shift2Rail Joint Undertaking projects In2Rail and In2Smart. The measurement data of the railway switches is provided by Strukton Rail

Kontinuierliche Überwachung der LST mit eingebetteten Sensoren

Der unerwartete Ausfall eines Elements der Leit- und Sicherungstechnik (LST) wie z. B. einer hochfrequentierten Weiche führt in der Regel unmittelbar zu negativen Auswirkungen auf den Bahnbetrieb und damit auf die Attraktivität des Verkehrsträgers Bahn. Daher ist die kontinuierliche Überwachung bekannter "kritischer Anlagen" der LST im laufenden Betrieb bereits heute Stand der Technik. So sind z. B. Messsysteme für die Erfassung der elektrischen Leistungsaufnahme von Weichenantrieben seit vielen Jahren am Markt verfügbar und bei einigen europäischen Eisenbahninfrastrukturbetreibern bereits flächendeckend im Einsatz bzw. werden gegenwärtig ausgerollt (z. B. [1]). Die kontinuierliche Zustandsüberwachung im laufenden Betrieb stellt dabei eine Grundvoraussetzung für das übergeordnete Ziel dar: die prädiktive Instandhaltung. Basierend auf dem aktuellen Zustand der Anlagen und Prognosen zur weiteren Entwicklung des Anlagenverhaltens sollen Instandhaltungsmaßnahmen frühzeitig geplant und zum ökonomisch optimalen Zeitpunkt durchgeführt werden [2]. Dadurch sollen auch unerwartete Ausfälle der Anlagen vermieden werden. In diesem Zusammenhang besteht hoher Forschungsbedarf in Bezug auf das Verständnis der Fehlzustände und deren Entwicklung, der zu erhebenden (Mess-) Daten sowie bei der Entwicklung und Nutzung automatischer Verfahren (insbesondere der Künstlichen Intelligenz – KI) zur Detektion [3], Diagnose [4] und Prognose [5] des Anlagenverhaltens. Im Folgenden werden Forschungsschwerpunkte des DLR-Instituts für Verkehrssystemtechnik (DLR-TS) für die Erkennung auffälligen Anlagenverhaltens (Anomaliedetektion) sowie für die Zustandsdiagnose am Beispiel der Weiche vorgestellt. Diese Forschungsarbeiten erfolgen in Kooperation mit internationalen Praxispartnern und sind in das europäische Joint-Undertaking Shift2Rail eingebettet. Die hier vorgestellten Arbeiten entstammen u. a. dem Leuchtturmprojekt In2Rail ([6], Grant 635900 im EU-Forschungsprogramm Horizont 2020) sowie dem Shift2Rail-Projekt In2Smart ([7], Grant 730569)

Anomaly detection for railway switch monitoring data to enable condition-based maintenance

Railway switches are a crucial railway asset since they enable train operators to route trains by changing tracks. A switch failure can compromise the availability of a larger part of the infrastructure in cases when train traffic heavily relies on that switch. Nearly one third of the total costs of railway maintenance is spent for switches and crossings. Thus there is a need for increased switch reliability and costs reduction. Nowadays tens of thousands assets around the world are remotely monitored. Switch monitoring data in combination with weather information can be combined to characterize switch functioning and to determine how it is influenced by the weather. Such a characterization allows the detection of anomalies i.e. emerging and sudden failures under different weather conditions. In this paper results from data-based switch-specific models for anomaly detection, which account for temperature influence, are presented. These results are validated against two annotated data sets based on experts' assessment. It is found that the model capabilities strongly depend from switch to switch. A model trained with features (derived from the monitoring data) that are narrowly distributed within small temperature intervals has a very good performance; otherwise the performance is poor. Additionally the influence of rain and humidity on the switch functioning was explored by including data from the closest weather station. No correlation was found probably due to the fact that the available weather information is only a proxy of the local conditions at the switch, stressing the importance of measuring weather parameters at the asset